lmscan 0.2.0

Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline.

🔍 lmscan

Detect AI-generated text. Fingerprint which LLM wrote it. Open-source GPTZero alternative.

GPTZero charges $15/month. Originality.ai charges per scan. Turnitin locks you into institutional contracts.

lmscan is free, open-source, works offline, and tells you which model wrote the text.

$ lmscan "In today's rapidly evolving digital landscape, it's important
to note that artificial intelligence has become a pivotal force in
transforming how we navigate the complexities of modern life..."

🔍 lmscan v0.1.0 — AI Text Forensics
══════════════════════════════════════════════════

  Verdict:     🤖 Likely AI (77% confidence)
  Words:       184
  Sentences:   10
  Scanned in 0.01s

┌────────────────────────────┬──────────┬────────────────────┐
│ Feature                    │ Value    │ Signal             │
├────────────────────────────┼──────────┼────────────────────┤
│ Burstiness                 │ 0.07     │ 🔴 Very low (AI)    │
│ Sentence length variance   │ 0.27     │ 🟡 Below average    │
│ Slop word density          │ 20.7%    │ 🔴 High (AI)        │
│ Transition word ratio      │ 2.2%     │ 🟡 Elevated         │
│ Readability consistency    │ 0.00     │ 🔴 Very low (AI)    │
│ ...                        │          │                     │
└────────────────────────────┴──────────┴────────────────────┘

🔎 Model Attribution
  1. GPT-4 / ChatGPT    62% — "delve", "tapestry", "beacon", "landscape" (×2), +19 more
  2. Claude (Anthropic)  13% — "robust", "nuanced", "comprehensive"
  3. Gemini (Google)      9% — "furthermore", "additionally"

⚠️  Flags
  • Very low burstiness (0.07) — AI text is more uniform in complexity
  • High slop word density (20.7%) — contains known AI vocabulary markers

Install

pip install lmscan

Zero dependencies. Works with Python 3.9+. No API keys. No internet. No GPU.

Usage

# Scan text directly
lmscan "Your text here..."

# Scan a file
lmscan document.txt

# Pipe from stdin
cat essay.txt | lmscan -

# JSON output (for scripts and CI)
lmscan document.txt --format json

# Per-sentence breakdown
lmscan document.txt --sentences

# CI gate: fail if AI probability > 50%
lmscan submission.txt --threshold 0.5

Python API

from lmscan import scan

result = scan("Text to analyze...")

print(f"AI probability: {result.ai_probability:.0%}")
print(f"Verdict: {result.verdict}")
print(f"Confidence: {result.confidence}")

# Which model wrote it?
for model in result.model_attribution:
    print(f"  {model.model}: {model.confidence:.0%}")
    for evidence in model.evidence[:3]:
        print(f"    → {evidence}")

# Per-sentence analysis
for sentence in result.sentence_scores:
    if sentence.ai_probability > 0.7:
        print(f"  🤖 {sentence.text[:60]}... ({sentence.ai_probability:.0%})")

Scan entire directories

from lmscan import scan_file
import glob

for path in glob.glob("submissions/*.txt"):
    result = scan_file(path)
    print(f"{path}: {result.verdict} ({result.ai_probability:.0%})")

How It Works

lmscan uses 12 statistical features derived from computational linguistics research to distinguish AI-generated text from human writing:

Feature	What it measures	AI signal
Burstiness	Variance in sentence complexity	AI text is unusually uniform
Sentence length variance	How much sentence lengths vary	AI produces uniform lengths
Vocabulary richness	Type-token ratio (Yule's K corrected)	AI reuses words more
Hapax legomena ratio	Fraction of words appearing once	AI has fewer unique words
Zipf deviation	How word frequencies follow Zipf's law	AI deviates from natural distribution
Readability consistency	Flesch-Kincaid variance across paragraphs	AI maintains constant readability
Bigram/trigram repetition	Repeated word pairs and triples	AI repeats phrase structures
Transition word ratio	"however", "moreover", "furthermore"...	AI overuses transitions
Slop word density	Known AI vocabulary markers	"delve", "tapestry", "beacon"...
Punctuation entropy	Diversity of punctuation usage	AI is more predictable

Each feature produces a signal via sigmoid transformation. The weighted combination produces the final AI probability.

Model Fingerprinting

lmscan includes vocabulary fingerprints for 5 major LLM families:

Model	Distinctive markers
GPT-4 / ChatGPT	"delve", "tapestry", "landscape", "leverage", "multifaceted", "it's important to note"
Claude (Anthropic)	"certainly", "I'd be happy to", "straightforward", "I should note"
Gemini (Google)	"crucial", "here's a breakdown", "keep in mind"
Llama / Meta	"awesome", "fantastic", "hope this helps"
Mistral / Mixtral	"indeed", "moreover", "hence", "noteworthy"

Attribution uses weighted vocabulary matching, phrase detection, and hedging pattern analysis.

Accuracy & Limitations

What lmscan is good at:

• Detecting text with strong AI stylistic patterns
• Identifying which model family generated text
• Scanning at scale (thousands of documents) with zero cost
• Providing explainable evidence (not a black box)

What lmscan cannot do:

• Detect AI text that has been manually edited or paraphrased
• Work reliably on very short text (<50 words)
• Detect AI text in non-English languages (English-only for now)
• Replace human judgment — use as a signal, not a verdict

This is statistical analysis, not a neural classifier. It detects stylistic patterns, not watermarks. It works best on unedited LLM output and degrades gracefully on edited text.

CI Integration

GitHub Actions

- name: AI Content Check
  run: |
    pip install lmscan
    lmscan submission.txt --threshold 0.7 --format json

Pre-commit

repos:
  - repo: https://github.com/stef41/lmscan
    rev: v0.1.0
    hooks:
      - id: lmscan
        args: ["--threshold", "0.7"]

Research Background

lmscan's approach is informed by published research on AI text detection:

• DetectGPT (Mitchell et al., 2023) — perturbation-based detection using log probability curvature
• GLTR (Gehrmann et al., 2019) — statistical visualization of token predictions
• Binoculars (Hans et al., 2024) — cross-model perplexity comparison
• Zipf's Law in NLP — word frequency distributions differ between human and AI text
• Stylometry — decades of authorship attribution research applied to AI forensics

lmscan takes the statistical intuitions from these papers and implements them as lightweight, dependency-free heuristics that work without requiring a reference language model.

FAQ

Q: Is this as accurate as GPTZero? A: GPTZero uses neural classifiers trained on labeled data. lmscan uses statistical heuristics. GPTZero is more accurate on edge cases; lmscan is free, offline, and explainable. Use both if accuracy matters.

Q: Can students use this to evade AI detection? A: lmscan shows which features trigger detection, which could help someone understand why text reads as AI-generated. This is by design — understanding AI writing patterns makes everyone a better writer. The same information is available in published research papers.

Q: Does it work on non-English text? A: Currently English-only. The slop word lists and transition word lists are English-specific. Statistical features (entropy, burstiness) work across languages but haven't been calibrated.

Q: Does it phone home? A: No. Zero network requests. No telemetry. No API keys. Everything runs locally.

Q: How is model attribution possible without running the model? A: Each LLM family has characteristic vocabulary biases. GPT-4 loves "delve" and "tapestry". Claude says "I'd be happy to". These are statistical fingerprints — not guaranteed attribution, but strong signals.

See Also

• reverse-SynthID — Reverse-engineering Google's image watermarking
• vibesafe — AI code safety scanner
• injectionguard — Prompt injection detection
• vibescore — Grade your vibe-coded project

License

Apache-2.0